Fine-grained image classification is challenging due to the large intra-class variance and
small inter-class variance, aiming at recognizing hundreds of sub-categories belonging to …

Fine-grained image classification is to recognize hundreds of subcategories belonging to
the same basic-level category, such as 200 subcategories belonging to the bird, which is …

In this paper, we propose a fine-grained image categorization system with easy deployment.
We do not use any object/part annotation (weakly supervised) in the training or in the testing …

Fine-grained image classification is to recognize hundreds of subcategories in each basic-
level category. Existing methods employ discriminative localization to find the key …

Fine-grained image classification is a challenging task due to the large intra-class variance
and small inter-class variance, aiming at recognizing hundreds of sub-categories belonging …

Given a training dataset composed of images and corresponding category labels, deep
convolutional neural networks show a strong ability in mining discriminative parts for image …

Fine-grained image recognition is a longstanding computer vision challenge that focuses on
differentiating objects belonging to multiple subordinate categories within the same meta …

Weakly supervised fine-grained image classification (WFGIC) aims at learning to recognize
hundreds of subcategories in each basic-level category with only image level labels …

We aim to provide a computationally cheap yet effective approach for fine-grained image
classification (FGIC) in this letter. Unlike previous methods that rely on complex part …

Multi-instance learning (MIL) is widely acknowledged as a fundamental method to solve
weakly supervised problems. While MIL is usually effective in standard weakly supervised …